segregation.multigroup.MultiDiversity

class segregation.multigroup.MultiDiversity(data, groups, w=None, normalized=False, network=None, distance=None, decay=None, precompute=None, function='triangular', **kwargs)

Multigroup Diversity Index.

Parameters:

datapandas.DataFrame or geopandas.GeoDataFrame, required

dataframe or geodataframe if spatial index holding data for location of interest

groupslist, required

list of columns on dataframe holding population totals for each group

wlibpysal.weights.KernelW, optional

lipysal spatial kernel weights object used to define an egohood

networkpandana.Network

pandana Network object representing the study area

distanceint

Maximum distance (in units of geodataframe CRS) to consider the extent of the egohood

decaystr

type of decay function to apply. Options include

precomputebool

Whether to precompute the pandana Network object

normalizedbool. Default is False.

Whether the resulting index will be divided by its maximum (natural log of the number of groups)

Notes

Based on Reardon, Sean F., and Glenn Firebaugh. “Measures of multigroup segregation.” Sociological methodology 32.1 (2002): 33-67 and Theil, Henry. “Statistical decomposition analysis; with applications in the social and administrative sciences”. No. 04; HA33, T4.. 1972.

This is also know as Theil’s Entropy Index (Equation 2 of page 37 of Reardon, Sean F., and Glenn Firebaugh. “Measures of multigroup segregation.” Sociological methodology 32.1 (2002): 33-67)

High diversity means less segregation.

Reference: [Reardon and Firebaugh, 2002].

Attributes:

statisticfloat

Multigroup Dissimilarity Index value

core_dataa pandas DataFrame

DataFrame that contains the columns used to perform the estimate.

__init__(data, groups, w=None, normalized=False, network=None, distance=None, decay=None, precompute=None, function='triangular', **kwargs)

Init.

Methods

__init__(data, groups[, w, normalized, ...])

Init.